GPU Comparison

NVIDIA H200 vs B200

Compare NVIDIA H200 and B200 across architecture, GPU memory, bandwidth, AI inference, training, infrastructure requirements and practical workload fit.

Daya ShankarLast verified: August 11, 2026Research methodology

NVIDIA Hopper

H200

GPU Memory

141 GB HBM3e

Bandwidth

4.8 TB/s

Memory-enhanced Hopper accelerator for large LLM inference, training and memory-intensive HPC.

Explore NVIDIA H200
VS

NVIDIA Blackwell

B200

GPU Memory

180 GB HBM3e

Bandwidth

Up to 8 TB/s

Blackwell-generation accelerator for frontier AI training, FP4/FP8 inference and large-scale AI systems.

Explore NVIDIA B200

H200 vs B200 at a Glance

Start with workload fit, then validate the choice against the exact cloud configuration and pricing available to you.

Choose H200 when

141 GB already fits the workload.

Choose B200 when

You want native FP4.

Compare the full workload

Memory, bandwidth, precision, interconnects, media features, power and cloud price can all change the right answer.

NVIDIA H200 vs NVIDIA B200 Specifications

H100, H200 and A100 use SXM figures. B200 uses current NVIDIA HGX B200 specifications. L40S and L4 use their native PCIe card specifications, so each product is represented in its primary deployment form.

SpecificationNVIDIA H200NVIDIA B200
ArchitectureNVIDIA HopperNVIDIA Blackwell
GPU Memory141 GB HBM3e180 GB HBM3e
Memory Bandwidth4.8 TB/sUp to 8 TB/s
FP3267 TFLOPS≈75 TFLOPS†
TF32 Tensor Core989 TFLOPS*≈2.25 PFLOPS*†
FP16 / BF16 Tensor Core1,979 TFLOPS*≈4.5 PFLOPS*†
FP8 Tensor Core3,958 TFLOPS*≈9 PFLOPS*†
FP4 Tensor CoreNot natively supported≈18 PFLOPS sparse / 9 PFLOPS dense†
NVLink900 GB/s1.8 TB/s
MIGUp to 7 MIGs @ 18 GBUp to 7 MIGs; 1g profile starts at 23 GB
Maximum PowerUp to 700 WUp to 1,000 W in DGX B200
Form FactorSXMSXM (HGX B200)

* Tensor Core values marked with an asterisk are NVIDIA sparse specifications where applicable; dense performance is lower.

† B200 per-GPU compute figures are derived from NVIDIA's published 8-GPU HGX B200 totals by dividing by eight. Memory, bandwidth and NVLink figures are published per GPU.

What Is the Main Difference Between NVIDIA H200 and NVIDIA B200?

H200 is the memory-focused peak of Hopper, while B200 moves to the Blackwell architecture. Both use HBM3e, but B200 increases capacity from 141 GB to 180 GB and bandwidth from 4.8 TB/s to as much as 8 TB/s per GPU.

B200 also introduces fifth-generation Tensor Cores with native FP4 support and fifth-generation NVLink at 1.8 TB/s per GPU in HGX B200. H200 uses fourth-generation Hopper Tensor Cores and 900 GB/s NVLink.

H200 remains compelling when 141 GB is enough and the workload is already tuned for Hopper. B200 is the stronger new-platform choice when low-precision AI compute, scale-up bandwidth and maximum per-GPU memory throughput matter.

Memory Capacity and Bandwidth

These specifications affect model fit, KV-cache headroom, batch size and memory-bound workloads.

GPU Memory

141 GB HBM3e vs 180 GB HBM3e
H200B200

Memory Bandwidth

4.8 TB/s vs Up to 8 TB/s
H200B200

H200 vs B200 for LLM Inference

Both are excellent for large LLM serving, but B200 adds more memory, much higher bandwidth and native FP4. H200 remains a strong choice for Hopper-optimized serving stacks and already offers a large 141 GB memory pool.

H200 vs B200 for AI Training

B200's newer Tensor Cores, larger memory and faster NVLink make it better suited to frontier training and large distributed runs. H200 remains strong when training is memory constrained but Blackwell is not required.

H200 vs B200 for HPC and Memory-Bound Workloads

H200 already offers 4.8 TB/s memory bandwidth. B200 raises that ceiling to up to 8 TB/s and provides faster GPU-to-GPU links, giving memory- and communication-heavy workloads more headroom.

Which GPU Fits Your Workload?

Use this as directional guidance. Benchmark your own model and software stack before making a large infrastructure commitment.

WorkloadH200B200Direction
Large LLM inferenceExcellentBest fitB200
Long-context servingExcellentBest fitB200
Frontier trainingExcellentBest fitB200
Hopper-optimized production stackBest fitExcellentH200 may be simpler
Memory-bound HPCExcellentBest fitB200
MIG / shared accelerationExcellentExcellentBoth
Lower accelerator power envelopeBest fitHigher powerH200

Cloud Pricing

Compare Current Provider Pricing

There is no single cloud price for either GPU. Rates vary by provider, region, server configuration, billing model and commitment. Compare current provider offers after you know which hardware class fits the workload.

H200 providers

Compare H200 pricing

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B200 providers

Compare B200 pricing

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Final Decision

Should You Choose NVIDIA H200 or NVIDIA B200?

Choose NVIDIA H200 if:

  • 141 GB already fits the workload.
  • You want Hopper compatibility with substantially more memory than H100.
  • A 700 W maximum power profile better matches your infrastructure.
  • Your provider offers H200 at stronger workload economics.

Choose NVIDIA B200 if:

  • You want native FP4.
  • You need the highest memory bandwidth of these two GPUs.
  • You are building a new Blackwell-scale training or inference cluster.
  • Fifth-generation NVLink and additional memory capacity matter.

H200 vs B200 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA H200 or B200?
Choose B200 for Blackwell FP4/FP8 performance, 180 GB memory, up to 8 TB/s bandwidth and fifth-generation NVLink. Choose H200 when Hopper's 141 GB HBM3e already solves the memory problem and a lower-power, established platform is preferable.
What is the main difference between H200 and B200?
H200 uses Hopper with 141 GB HBM3e and 4.8 TB/s memory bandwidth, while B200 uses Blackwell with 180 GB HBM3e and Up to 8 TB/s. Tensor Core generation, precision support, interconnects and power can also differ.
Is H200 or B200 better for LLM inference?
Both are excellent for large LLM serving, but B200 adds more memory, much higher bandwidth and native FP4. H200 remains a strong choice for Hopper-optimized serving stacks and already offers a large 141 GB memory pool.
Which GPU is better for AI training, H200 or B200?
B200's newer Tensor Cores, larger memory and faster NVLink make it better suited to frontier training and large distributed runs. H200 remains strong when training is memory constrained but Blackwell is not required.
How much memory do H200 and B200 have?
NVIDIA H200 provides 141 GB HBM3e, while NVIDIA B200 provides 180 GB HBM3e. Memory capacity alone does not determine performance, so bandwidth, precision support and workload behavior should also be considered.
Which is cheaper to rent, H200 or B200?
Cloud rental pricing for H200 and B200 varies by provider, region, configuration and billing model. Check current provider pricing rather than assuming one GPU is always cheaper.

Sources & Verification

Hardware specifications were checked against official NVIDIA product pages and documentation.

Last verified: August 11, 2026 · View our data source standards

GPU Profile

NVIDIA H200

See specifications, cloud providers, pricing and workload fit for H200.

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GPU Profile

NVIDIA B200

See specifications, cloud providers, pricing and workload fit for B200.

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